In conventional computer systems used to predict the amount of renewable energy to be provided by an electric utility for a given day, technical issues exist in regards to obtaining an accurate prediction for the amount of solar energy and/or wind energy available to the utility for that day. These technical problems relate to how the systems used in such predictions identify, obtain, and interact with other computer systems within the electric grid environment, such as the system operator computer system and various utility computer systems, to obtain relevant data points, and to the processes used to make such predictions accurate.
Current computer systems for providing forecasts of energy generated by renewable energy sources, such as wind power and solar power, whether they be day-ahead, hour-ahead, or even five minutes ahead, are deficient in that they provide forecasts that are inferior to a persistence forecast that naively assumes that the wind energy output in period t will be equal to the level of wind energy in prior period t−1. In fact, it is conventionally believed, based on capacity weighted forecast accuracy metrics, for example, that improvements in wind energy and solar energy forecasting have effectively eliminated the challenge posed by the intermittency of wind and solar energy. This deficiency is also generally present in computer systems for solar energy forecasts. As a result, the forecast inaccuracies associated with the current forecasting computer systems pose a challenge to achieving efficient balancing of supply and demand for electric power on a continuous basis.